System and method for web destination profiling

ABSTRACT

An improved system and method for web destination profiling for online population-targeted advertising is provided. A web destination profiler may be provided for generating web destination profiles. Traffic may be analyzed at a particular web destination in order to understand the population visiting the web destination. The analysis of user traffic, including differentiated clickstream data, may be applied for determining known characteristics of a web destination profile. Moreover, unknown characteristics of a web destination profile may be determined using a variety of techniques including inferring characteristics by modeling traffic flow through other web destinations, estimating characteristics from other web destination profiles by predicting traffic flow through other web destinations, propagating characteristics to a web destination profile by smoothing a joint distribution of characteristics of other web destination profiles, and so forth. Web destination profiles may be used by applications such as an online application for population-targeted advertising.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present invention is related to the following United States patentapplication, filed concurrently herewith and incorporated herein in itsentirety:

“System and Method for Population-Targeted Advertising,” U.S. patentapplication Ser. No. 11/495,269.

FIELD OF THE INVENTION

The invention relates generally to computer systems, and moreparticularly to an improved system and method for web destinationprofiling for online population-targeted advertising.

BACKGROUND OF THE INVENTION

Operators of websites offering online content may manage an inventory ofadvertisements that may be shown to visitors viewing content of awebsite. When a user may visit a website, the operator of the website ora third party may choose to show one or more advertisements to the userwith the expectation that the user may select an advertisement to buyadvertised goods or services. Advertisers may bid to have theiradvertisement shown to a visitor viewing particular content of thewebsite. Or the operator of the website or third party may choose theadvertisement and may generate revenue whenever a visitor may select anadvertisement shown while viewing content of the website.

For some visitors of a website, properties of the visitor may be knownand can be used for selecting an advertisement by matching the knownproperties of the visitor to the content being viewed. However, a vastmajority of visitors may be unknown, and there may not be any knownproperties about the visitor that may be used for selecting anadvertisement based upon content matching. Without any known propertiesfor the vast majority of visitors, operators are unable to selectadvertisements that may be of interest to particular visitors based uponsome known properties of who may be viewing content at the website. As aresult, operators of such websites remain unlikely to be able tooptimize revenue generation for selecting advertisements based oncontent matching, especially where revenue generation relies uponclick-through rates of visitors.

What is needed is a system and method that may allow operators ofwebsites offering online content to select advertisements for unknownvisitors that may be of interest to particular visitors. Such a systemand method should also be able to select advertisements for knownvisitors viewing content at the website.

SUMMARY OF THE INVENTION

Briefly, the present invention may provide a system and method for webdestination profiling for online population-targeted advertising. A webdestination profiler may be provided in an embodiment that may includean operably coupled traffic analysis engine for analyzing traffic at aweb destination, a clickstream analysis engine for analyzing clickstreamdata from a web destination, a topology analysis engine for analyzingtopology data about a web destination, and a smoothing engine forpropagating characteristics to a web destination profile in a variety ofways, including from other web destination profiles, from link analysisof the connectivity of the website with other websites, from trafficanalysis of the traffic between pages of the website and other pages,either on of off the website, from analysis of content of the pages ormetadata such as tags to determine pages with similar content or tagselsewhere that may be used for smoothing. The traffic analysis enginemay include an operably coupled model generator for generating a modelof traffic flow among web destinations and a traffic flow analysisengine for propagating population characteristics to web destinationprofiles by predicting traffic flow through web destinations.

In general, the web destination profiler may provide services forgenerating web destination profiles. Traffic may be analyzed at aparticular web destination in order to understand the populationvisiting the web destination. The traffic at a particular webdestination may represent differentiated clickstream data andundifferentiated clickstream data. In addition, graph data and sitestructure information may also be used in analyzing traffic for a webdestination. The analysis of user traffic, including differentiatedclickstream data, may be applied for determining known characteristicsof a web destination profile. Moreover, the analysis of traffic,including undifferentiated clickstream data, may also be applied fordetermining unknown characteristics of a web destination profile. Inparticular, unknown characteristics of a web destination profile may bedetermined using a variety of techniques including inferringcharacteristics by modeling traffic flow through other web destinations,estimating characteristics from other web destination profiles bypredicting traffic flow through other web destinations, propagatingcharacteristics to a web destination profile by smoothing a jointdistribution of characteristics of other web destination profiles, andso forth.

Web destination profiles may be stored in storage for use byapplications such as an online application for population-targetedadvertising. For instance, an application that may displayadvertisements to users who visit a web destination, including managedcontent properties, may use the present invention to selectadvertisements using web destination profiles for display of theadvertisements with content of the web destinations. Advantageously, anadvertisement may be selected using the web destination profile wherethe system may have limited knowledge of the content of the webdestination and/or the profile of the particular user visiting the webdestination. Moreover, when traffic may not be observed or inferred, anapproximate profile of characteristics may be generated by an analysisof the content of a web destination. In this way, unknown profilecharacteristics may be propagated from other web destination profiles ofsimilar web destinations. Other advantages will become apparent from thefollowing detailed description when taken in conjunction with thedrawings, in which:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram generally representing a computer system intowhich the present invention may be incorporated;

FIG. 2 is a block diagram generally representing an exemplaryarchitecture of system components in an embodiment for web destinationprofiling for online population-targeted advertising, in accordance withan aspect of the present invention;

FIG. 3 is a flowchart generally representing the steps undertaken in oneembodiment for web destination profiling, in accordance with an aspectof the present invention;

FIG. 4 is an illustration depicting in an embodiment clickstream trailswithin a set of web pages, in accordance with an aspect of the presentinvention;

FIG. 5 is a flowchart generally representing the steps undertaken in oneembodiment for determining unknown characteristics of a web destinationprofile, in accordance with an aspect of the present invention;

FIG. 6 is an illustration depicting in an embodiment web destinationprofile characteristics propagated in a hierarchy of a set of web pagesby smoothing, in accordance with an aspect of the present invention;

FIG. 7 is a block diagram generally representing an exemplaryarchitecture of system components in an embodiment for onlinepopulation-targeted advertising, in accordance with an aspect of thepresent invention; and

FIG. 8 is a flowchart generally representing the steps undertaken in oneembodiment for online population-targeted advertising, in accordancewith an aspect of the present invention.

DETAILED DESCRIPTION

Exemplary Operating Environment

FIG. 1 illustrates suitable components in an exemplary embodiment of ageneral purpose computing system. The exemplary embodiment is only oneexample of suitable components and is not intended to suggest anylimitation as to the scope of use or functionality of the invention.Neither should the configuration of components be interpreted as havingany dependency or requirement relating to any one or combination ofcomponents illustrated in the exemplary embodiment of a computer system.The invention may be operational with numerous other general purpose orspecial purpose computing system environments or configurations.

The invention may be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by a computer. Generally, program modules include routines,programs, objects, components, data structures, and so forth, whichperform particular tasks or implement particular abstract data types.The invention may also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed computingenvironment, program modules may be located in local and/or remotecomputer storage media including memory storage devices.

With reference to FIG. 1, an exemplary system for implementing theinvention may include a general purpose computer system 100. Componentsof the computer system 100 may include, but are not limited to, a CPU orcentral processing unit 102, a system memory 104, and a system bus 120that couples various system components including the system memory 104to the processing unit 102. The system bus 120 may be any of severaltypes of bus structures including a memory bus or memory controller, aperipheral bus, and a local bus using any of a variety of busarchitectures. By way of example, and not limitation, such architecturesinclude Industry Standard Architecture (ISA) bus, Micro ChannelArchitecture (MCA) bus, Enhanced ISA (EISA) bus, Video ElectronicsStandards Association (VESA) local bus, and Peripheral ComponentInterconnect (PCI) bus also known as Mezzanine bus.

The computer system 100 may include a variety of computer-readablemedia. Computer-readable media can be any available media that can beaccessed by the computer system 100 and includes both volatile andnonvolatile media. For example, computer-readable media may includevolatile and nonvolatile computer storage media implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules orother data. Computer storage media includes, but is not limited to, RAM,ROM, EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can accessed by the computer system 100. Communication mediamay include computer-readable instructions, data structures, programmodules or other data in a modulated data signal such as a carrier waveor other transport mechanism and includes any information deliverymedia. The term “modulated data signal” means a signal that has one ormore of its characteristics set or changed in such a manner as to encodeinformation in the signal. For instance, communication media includeswired media such as a wired network or direct-wired connection, andwireless media such as acoustic, RF, infrared and other wireless media.

The system memory 104 includes computer storage media in the form ofvolatile and/or nonvolatile memory such as read only memory (ROM) 106and random access memory (RAM) 110. A basic input/output system 108(BIOS), containing the basic routines that help to transfer informationbetween elements within computer system 100, such as during start-up, istypically stored in ROM 106. Additionally, RAM 110 may contain operatingsystem 112, application programs 114, other executable code 116 andprogram data 118. RAM 110 typically contains data and/or program modulesthat are immediately accessible to and/or presently being operated on byCPU 102.

The computer system 100 may also include other removable/non-removable,volatile/nonvolatile computer storage media. By way of example only,FIG. 1 illustrates a hard disk drive 122 that reads from or writes tonon-removable, nonvolatile magnetic media, and storage device 134 thatmay be an optical disk drive or a magnetic disk drive that reads from orwrites to a removable, a nonvolatile storage medium 144 such as anoptical disk or magnetic disk. Other removable/non-removable,volatile/nonvolatile computer storage media that can be used in theexemplary computer system 100 include, but are not limited to, magnetictape cassettes, flash memory cards, digital versatile disks, digitalvideo tape, solid state RAM, solid state ROM, and the like. The harddisk drive 122 and the storage device 134 may be typically connected tothe system bus 120 through an interface such as storage interface 124.

The drives and their associated computer storage media, discussed aboveand illustrated in FIG. 1, provide storage of computer-readableinstructions, executable code, data structures, program modules andother data for the computer system 100. In FIG. 1, for example, harddisk drive 122 is illustrated as storing operating system 112,application programs 114, other executable code 116 and program data118. A user may enter commands and information into the computer system100 through an input device 140 such as a keyboard and pointing device,commonly referred to as mouse, trackball or touch pad tablet, electronicdigitizer, or a microphone. Other input devices may include a joystick,game pad, satellite dish, scanner, and so forth. These and other inputdevices are often connected to CPU 102 through an input interface 130that is coupled to the system bus, but may be connected by otherinterface and bus structures, such as a parallel port, game port or auniversal serial bus (USB). A display 138 or other type of video devicemay also be connected to the system bus 120 via an interface, such as avideo interface 128. In addition, an output device 142, such as speakersor a printer, may be connected to the system bus 120 through an outputinterface 132 or the like computers.

The computer system 100 may operate in a networked environment using anetwork 136 to one or more remote computers, such as a remote computer146. The remote computer 146 may be a personal computer, a server, arouter, a network PC, a peer device or other common network node, andtypically includes many or all of the elements described above relativeto the computer system 100. The network 136 depicted in FIG. 1 mayinclude a local area network (LAN), a wide area network (WAN), or othertype of network. Such networking environments are commonplace inoffices, enterprise-wide computer networks, intranets and the Internet.In a networked environment, executable code and application programs maybe stored in the remote computer. By way of example, and not limitation,FIG. 1 illustrates remote executable code 148 as residing on remotecomputer 146. It will be appreciated that the network connections shownare exemplary and other means of establishing a communications linkbetween the computers may be used.

Web Destination Profiling for Population-Targeted Advertising

The present invention is generally directed towards a system and methodfor web destination profiling for online population-targetedadvertising. As used herein, a web destination may mean a web page, adirectory, a web site, a collection of sites, or other destination. Aweb destination profile may mean a set or distribution ofcharacteristics including demographic, geographic, and/or psychographicthat may be associated with a population visiting a web destination. Thepresent invention may provide a web destination profiler that maygenerate web destination profiles by analyzing traffic at a particularweb destination in order to understand the population visiting the webdestination. Traffic, as used herein, may mean visits to a webdestination by users. The analysis of user traffic may be applied fordetermining known characteristics of a web destination profile and fordetermining unknown characteristics of a web destination profile using avariety of techniques. Even when traffic may not be observed orinferred, unknown profile characteristics may be propagated from otherweb destination profiles of similar web destinations.

As will be seen, applications that may display advertisements to userswho visit a web destination, including managed content properties, mayuse the present invention to select advertisements using web destinationprofiles when the system may have limited knowledge of the content ofthe web destination and/or the profile of the particular user visitingthe web destination. As will be understood, the various block diagrams,flow charts and scenarios described herein are only examples, and thereare many other scenarios to which the present invention will apply.

Turning to FIG. 2 of the drawings, there is shown a block diagramgenerally representing an exemplary architecture of system components inan embodiment for web destination profiling. Those skilled in the artwill appreciate that the functionality implemented within the blocksillustrated in the diagram may be implemented as separate components orthe functionality of several or all of the blocks may be implementedwithin a single component. For example, the functionality for theclickstream analysis engine 206 may be included in the same component asthe traffic analysis engine 210. Or the functionality of the trafficflow analysis engine 214 may be implemented as a separate component fromthe traffic analysis engine 210. Moreover, those skilled in the art willappreciate that the functionality implemented within the blocksillustrated in the diagram may be executed on a single computer ordistributed across a plurality of computers for execution.

In various embodiments, a server 202, such as computer system 100 ofFIG. 1, may include a web destination profiler 204 operably coupled tostorage 220. In general, the web destination profiler 204 may be anytype of executable software code such as a kernel component, anapplication program, a linked library, an object with methods, and soforth. The storage 220 may be any type of computer-readable media andmay store topology data 222, clickstream data 224, user profiles 226,and web destination profiles 228.

The web destination profiler 204 may provide services for generating webdestination profiles 228. The web destination profiler 204 may include atopology analysis engine 208 for analyzing topology data 222 about webdestinations, a clickstream analysis engine 206 for analyzingclickstream data 224, a traffic analysis engine 210 for analyzingtraffic and user profiles 226, a smoothing engine 216 for propagatingcharacteristics to a web destination profile from other web destinationprofiles, and a profile locator 218 for locating a web destinationprofile for a web destination. The traffic analysis engine 210 mayinclude a model generator 212 for generating a model of traffic flowamong web destinations for propagating population characteristics to webdestinations and a traffic flow analysis engine 214 for propagatingpopulation characteristics to web destination profiles by predictingtraffic flow through web destinations. Each of these modules may also beany type of executable software code such as a kernel component, anapplication program, a linked library, an object with methods, or othertype of executable software code.

There may be a variety of applications which may use web destinationprofiling for online population-targeted advertising. These applicationsmay optimize revenue based on a set of characteristics determined forvisitors of a particular web destination. For instance, consider thatmany advertisements may be much more likely to be clicked on by certaintypes of users: males versus females, old versus young, Asian versusEuropean, interested in digital cameras, and so forth. There may be manypossible characterizations of a user which may be predictive of thelikelihood of the user clicking on a particular advertisement. Assumingthat such characteristics exist, it may not be necessary to know whatthe specific characteristics may be. It may be sufficient, that for alimited class of users, the value of a particular characteristic beknown.

In various embodiments, the system may attempt to analyze the traffic ata particular web destination—this could be a web page, a site, adirectory, a collection of sites, or some other destination. The trafficarriving at a destination may be drawn from some distribution. Forinstance, some fraction of the visitors may be 27-year-old malesinterested in photography, and some fraction may be from various otherclasses. Such a set of fractions may therefore be represented as adistribution over several variables that may include, for example, theage of a visitor as well as the gender and particular interests. Byestimating a joint distribution over a particular set of characteristicvariables for visitors to a particular web destination, the overallpopulation visiting the web destination may be understood, andadvertisements may be selected by understanding the population visitingthe web destination rather than by only understanding a known uservisiting the web destination. This technique may be referred to aspopulation-targeted advertising as the goal may be to understand thepopulation visiting a web destination and then to treat the current useras a representative of that overall population. The populationinformation may be used in two ways. First, an advertisement may beselected so that the expected revenue over the population distributionmay be maximized. Such a use of the population information may beapplicable for an application where there may be little or noinformation about the current user. Or, the population may be employedas a prior distribution in attempting to estimate properties of thecurrent user, based on some user-specific observation such as the recentbrowsing history of the user. These estimated properties of a currentuser may then be applied to select an advertisement.

FIG. 3 presents a flowchart generally representing the steps undertakenin one embodiment for performing web destination profiling. At step 302,user traffic may be analyzed at a particular web destination. Ingeneral, the user traffic at a particular web destination may representdifferentiated clickstream data and undifferentiated clickstream data.For example, FIG. 4 presents an illustration depicting in an embodimentclickstream trails within a set of web pages 402. A clickstream trailmay mean a path from a web destination through one or more intermediateweb destinations to another web destination. There may be two types ofclickstream trails illustrated in FIG. 4: a differentiated clickstreamtrail 404 representing differentiated clickstream data and anundifferentiated clickstream trail 406 representing undifferentiatedclickstream data.

The differentiated clickstream data may represent a small fraction ofuser traffic with information about some characteristics of each user.Differentiated clickstream data may be augmented with some of thecharacteristics of each user that may be available from a user profile.For example, a user profile may be accessible during a user session fora user who may have logged in to a web site and may have provided userprofile information. In addition, the system may have access toinformation from an Internet Service Provider (ISP) and the clickstreamtrails of undifferentiated users may be examined to determine if any mayhave logged in to a trusted system from which profile information for auser may be available. Even if the user may not have logged in, machinelearning may alternatively be applied to predict the characteristics ofa user based on the behavior of that user, particularly if the usersession may be long-lasting or includes highly discriminative behavior.

The undifferentiated clickstream data may represent a large volume ofuser traffic whose characteristics may be unknown, yet its clickstreamtrails may be known. Undifferentiated clickstream data may be gatheredin two primary ways: user-based samples and location-based samples.User-based samples may be produced when the behavior of a user may beavailable to the system. For instance, the behavior of a user may beavailable to a system if the system may be the ISP of the user and maybe authorized to make use of the behavioral information. Alternatively,the owner of the system might acquire ISP data. Moreover, an owner of asystem may also have other mechanisms to gather such data, such as atoolbar which may observe user behavior and transmit some or all of itback to the system.

Undifferentiated clickstream data may also be gathered by collectinglocation-based samples. Location-based samples may be produced wheneverusers may visit a particular website, and then the owner of the websitemay observe the behavior of the users while on the website. For example,the owner may observe which links the user clicks to navigate thewebsite and which links the user clicks in order to leave the website.In the context of a large search engine, location-based samples ofundifferentiated clickstream trails may provide a good understanding ofthe web destinations across the world wide web that are reachabledirectly via a search.

Those skilled in the art may appreciate that other information may beused in analyzing traffic for a web destination. Such information may,for example, include graph data and site structure information. Graphdata may represent ways users may reach a web destination includingbrowsing, searching, or bookmarks. Graph data may be available from asearch engine or other source that may produce a large-scale crawl ofthe web. Additionally, graph data may be available by querying an enginewhich supports inlink and outlink queries.

Site structure information may be available by global analysis of one ormore websites and may include the structure of Uniform Resource Locators(URLs). Such an analysis may reveal that two web sites may have the sameowner listed in their DNS records, or that the web sites may employ thesame template and therefore may likely be managed by the same entity.Similarly, a single site may have two sub-sites which are owned bydistinct individuals. This might be uncovered by particular known URLconstructions such as http://www.site.com/homes/jim, or by analysis ofthe inter-linking behavior on a site. The structure of URLs may alsoprovide a hierarchical view of the proximity of URLs. For example,http://www.site1.com/a/b and http://www.site2.com/a/c have in common theprefix http://www.site1.com/a, and may therefore be viewed as quitesimilar.

The analysis of user traffic, including differentiated clickstream data,may be applied at step 304 for determining known characteristics of aweb destination profile. A technique for doing so in an embodiment maybe to infer the characteristics of a visitor population by directobservation of a particular web destination, if a large number ofdifferentiated trails have passed through that web destination.

The analysis of user traffic, including undifferentiated clickstreamdata, may be applied at step 306 for determining unknown characteristicsof a web destination profile from other web destination profiles. Invarious embodiments, characteristics may be propagated from other webdestination profiles to determine unknown characteristics of a webdestination profile. For instance, characteristics may be inferred fromother web destinations by modeling traffic flow through the other webdestinations, characteristics may be estimated from other webdestinations by predicting traffic flow through the other webdestinations, characteristics may be propagated from other webdestination profiles by smoothing, and so forth. Further details of theimplementation in various embodiments for determining unknowncharacteristics of a web destination profile from other web destinationprofiles may be presented below in conjunction with the description ofFIG. 5.

After determining characteristics of a web destination profile, the webdestination profile may be stored in persistent storage at step 308 foruse by applications such as an online application forpopulation-targeted advertising, and processing may be finished forperforming web destination profiling.

FIG. 5 presents a flowchart generally representing the steps undertakenin one embodiment for determining unknown characteristics of a webdestination profile. At step 502, unknown profile characteristics may beinferred by modeling traffic flow through other web destinations. In anembodiment, a Markov model may be used to model traffic flow for a setof web pages. For example, consider X to be the set of objects in aprobability space of the joint distribution over all usercharacteristics. For example, an element of X may specify age, zipcode,as well as interests. Each user may therefore be represented by one ormore elements of X and X may be represented as a full materialization ofa joint distribution over several dimensions of characteristics, such asage, gender, geography, and interests. It should be noted that thetechniques may also operate in a more general setting, in which therepresentation of this joint distribution is implicit. In such arepresentation, techniques for smoothing the joint distribution may beemployed in an embodiment, so that the probability of a particularelement of X may be estimated before that element has been seen.

Consider a set of web pages, and a matrix M whose (i,j)^(th) entry maybe 1 if page i may link to page j, and 0 otherwise. Also consider od(i)to represent the out-degree of i: od(i)=|{j|M_(i,j)=1}|. Then considerM′_(i,j)=M_(i,j)/od(i); thus, M′ may represent a row-stochastic variantof M, which may be seen as the matrix of a Markov process. Next,consider M^(e) to be a variant of M′ which has been modified to beergodic, using for instance the approach described in U.S. patentapplication Ser. No. 11/474,195 (now U.S. Pat. No. 7,624,104) entitled:“User-Sensitive PageRank”, or any other technique. Then a unique vector{right arrow over (p)} may be computed with one entry per web page whosei^(th) entry may be the steady state probability that the Markov chainaccording to M^(e) may be at state i. Considering that the distributionof user characteristics at page i may be given by D_(i), then, by thedefinition of steady state, p_(j) may be represented as follows:

$p_{j} = {\sum\limits_{i{{M_{i,j}^{e} > 0}}}{p_{i}M_{i,j}^{e}}}$

Thus, the steady state of the markov process defined by M^(e) may inducea steady-state flow on each edge. A simple technique may be used toestimate the traffic flow in the presence of the link topology, but theabsence of any behavioral data. To begin with, a primary simplifyingassumption may be made: assume that if traffic departs from a web pageto three possible next states, each with equal probability of 1/3,traffic restricted to a subset of X will depart according to the samedistribution. That is, if half the visitors to a site depart byfollowing some link, then assume half the male visitors, and half the20-year-old visitors, may likewise depart by following that same link.By considering the next state to be independent of the usercharacteristics, topological information and undifferentiated clicktrails may be used in order to determine how particular populationcharacteristics may propagate.

Given this assumption, each element of X may then be viewed as aparticular commodity flowing through the network. If some p fraction ofthe total flow of the entire system may be represented in the steadystate as 40-year-old women visiting page u, and 1/3 of the flowdeparting page u may travel to page v, then at least a p/3 fraction ofthe total flow of the system may arrive at page v in the form of40-year-old women. However, other nodes may also send 40-year-old womento page v that may result in a higher overall concentration than p/3 inthe steady state. To characterize the distribution D_(j) at page j, anequation representative of a family of equations that may capture theflow of a particular commodity xεX into destination j may be defined asfollows:

${D_{j} = {\sum\limits_{i{{M_{i,j}^{e} > 0}}}{p_{i}M_{i,j}^{e}{D_{i}/p_{j}}}}},$where p_(j) may be seen to be the appropriate normalizing constant, andp_(i)M_(i,j) ^(e)D_(i) may be the relative fraction of users fromdistribution D_(i) who may flow along the edge (i,j) and then arrive atstate j.

Given a set of observations about D_(i) for various web pages i, theequation above may be employed to propagate this information throughoutthe remainder of the graph representing web destinations.

Unfortunately, there may be no consistent solution to these equations.One approach may be to simply write an iterative algorithm that may fixthe vectors D_(i) which may be pre-specified and that may iterativelyupdate the remaining vectors. Because this update technique makes use ofpre-specified values of D_(i), which may be known in advance, theupdated results may be normalized after each step by using a normalizingconstant C:

$D_{j}^{new} = \left\{ {\begin{matrix}{D_{j}} & {D_{j}\mspace{14mu}{pre}\text{-}{specified}} \\{\left( {\sum\limits_{i{{M_{i,j}^{e} > 0}}}{p_{i}M_{i,j}^{e}{D_{i}/p_{j}}}} \right)/C} & {otherwise}\end{matrix}.} \right.$

The initial values and the normalizing constant may be determined usingwell-known techniques to those skilled in the art.

In various embodiments, unknown profile characteristics may be estimatedat step 504 by predicting traffic flow through other web destinations.In one embodiment, a propagated distribution may be optimized bypredicting traffic flow. Consider each web page to have an observeddistribution D_(i), which may be unspecified for many web pages wherethere may not be a differentiated data point observed for those webpages. Also consider each web page to have a constant weight w_(i)reflecting the confidence in the measurement D_(i). A set ofdistributions may be selected to minimize the following quantity:

${{\sum\limits_{j}{w_{j}{d\left( {D_{j},{\overset{\sim}{D}}_{i}} \right)}}} + {\left( {1 - w_{j}} \right)d\text{(}{\overset{\sim}{D}}_{j}}},{\sum\limits_{i{{M_{i,j}^{e} > 0}}}{p_{i}M_{i,j}^{e}{{\overset{\sim}{D}}_{i}/{p_{j}.}}}}$

The first term in this sum may be the distance between the predicteddistribution and the observed distribution. The second term in this summay be the distance between the predicted distribution and thepropagated distribution determined by the traffic arriving at the page.A tractable approach for minimizing the quantity may be based on simpleiterations as suggested above. That is, an iterative algorithm may bewritten that may fix the vectors D_(i) which may be pre-specified andthat may iteratively update the remaining vectors.

The precise algorithm to be employed may depends on the distancemeasure, d(•,), that may be used. In an embodiment, the l₂-norm could beused and a large unconstrained quadratic minimization may be solved forthe set of equations. Other embodiments may employ the l₁-norm orl₁-norm measure. In either case, the distance may be represented by|{tilde over (d)}_(ij)−d_(ij)|.

The quantity,

${{\sum\limits_{j}{w_{j}{d\left( {D_{j},{\overset{\sim}{D}}_{i}} \right)}}} + {\left( {1 - w_{j}} \right)d\text{(}{\overset{\sim}{D}}_{j}}},{\sum\limits_{i{{M_{i,j}^{e} > 0}}}{p_{i}M_{i,j}^{e}{{\overset{\sim}{D}}_{i}/p_{j}}}},$may be minimized, for example, using the l_(∞)-norm by minimizing

${\sum\limits_{i}\left\{ {{w_{i}u_{i}} + {\left( {1 - w_{i}} \right)v_{i}}} \right\}},$where the non-negative variables u_(i) and v_(i) may be defined suchthat:

${{- u_{i}} \leq {{\overset{\sim}{d}}_{ij} - d_{ij}} \leq {u_{i}{\forall i}}},j,\mspace{14mu}{{{and} - v_{i}} \leq {{\overset{\sim}{d}}_{ij} - {\sum\limits_{i{{M_{i,j}^{e} > 0}}}{p_{i}M_{i,j}^{e}{{\overset{\sim}{D}}_{i}/p_{j}}}}} \leq {v_{i}{\forall i}}},{j.}$

The quantity,

${{\sum\limits_{j}{w_{j}{d\left( {D_{j},{\overset{\sim}{D}}_{i}} \right)}}} + {\left( {1 - w_{j}} \right)d\text{(}{\overset{\sim}{D}}_{j}}},{\sum\limits_{i{{M_{i,j}^{e} > 0}}}{p_{i}M_{i,j}^{e}{{\overset{\sim}{D}}_{i}/p_{j}}}},$may also be minimized in another embodiment using the l₁-norm byminimizing

${\sum\limits_{i}\left\{ {{w_{i}{\sum\limits_{j}u_{ij}}} + {\left( {1 - w_{i}} \right){\sum\limits_{j}v_{ij}}}} \right\}},$where the variables u and v may be defined as non-negative doublysubscripted variables such that:

${{- u_{ij}} \leq {{\overset{\sim}{d}}_{ij} - d_{ij}} \leq {u_{ij}{\forall i}}},j,\mspace{14mu}{{{and} - v_{ij}} \leq {{\overset{\sim}{d}}_{ij} - {\sum\limits_{i{{M_{i,j}^{e} > 0}}}{p_{i}M_{i,j}^{e}{{\overset{\sim}{D}}_{i}/p_{j}}}}} \leq {v_{ij}{\forall i}}},{j.}$

A set of equations for minimizing these quantities may be represented aslinear programs that may be potentially large but may be solved usingtechniques well-known by those skilled in the art.

In yet another embodiment, a maximum entropy formulation, for instanceas presented in J. A. Tomlin, “A New Paradigm for Ranking Pages on theWorld Wide Web”, pp 350-355, Proc. World Wide Web Conference 2003(WWW2003), Budapest, May 2003, may be employed for estimating probablesurfer distribution, subject to what may be known, such as the networktopology and the distribution of user characteristics at a subset of theweb pages. In its simplest form where strong connectedness may beassumed and there may not be any known characteristics of the populationthe visited the web pages, the number of users which may migrate fromweb page i to web page j, denoted as y_(ij) (≧0), may be constrained bythe equations:

${{{{\sum\limits_{j{{{({i,j})} \in E}}}y_{ij}} - {\sum\limits_{j{{{({j,i})} \in E}}}y_{ji}}} = {0\mspace{14mu}\left( {{i = 1},\ldots\mspace{11mu},n} \right)}};{{\sum\limits_{i,j}y_{ij}} = Y}},$where Y may be the total number of users. The maximum entropy may befound by maximizing the quantity:

$- {\sum\limits_{{({i,j})} \in E}{y_{ij}\log\;{y_{ij}.}}}$Considering that the total traffic of users into or out of some nodes jmay be known, the equations:

${{\sum\limits_{j{{{({i,j})} \in E}}}y_{ij}} - {\sum\limits_{j{{{({j,i})} \in E}}}y_{ji}}} = {0\mspace{14mu}\left( {{i = 1},\ldots\mspace{11mu},n} \right)}$may be replaced by the following pairs of equations:

${{\sum\limits_{{({j,i})} \in E^{\prime}}y_{ji}} = h_{i}},{{\sum\limits_{{({i,j})} \in E^{\prime}}y_{ij}} = h_{i}}$without significantly complicating the computational procedure for thisclass of problems.

Returning now to the population model, consider that the populationdescription D_(i) for node i may be mapped onto a vector ofcharacteristics, D_(i)=<d_(i1), d_(i2), . . . , d_(iK)>, where thesecond index denotes the characteristics k=1, . . . , K. An instance ofthe entropy model may then be defined for each characteristic k, havingflow variables y_(ij) ^(k) and constraints:

${{\sum\limits_{j{{{({i,j})} \in E}}}y_{ij}^{k}} - {\sum\limits_{j{{{({j,i})} \in E}}}y_{ji}^{k}}} = {{0\mspace{14mu}\left( {{i = 1},\ldots\mspace{11mu},n} \right)\mspace{14mu}{\sum\limits_{i,j}y_{ij}^{k}}} = Y^{k}}$where Y^(k) may be the total population of users of type k. For thoseweb pages for which user characteristic information may be known, theequations, which may be represented by

${{{\sum\limits_{j{{{({i,j})} \in E}}}y_{ij}^{k}} - {\sum\limits_{j{{{({j,i})} \in E}}}y_{ji}^{k}}} = {0\mspace{14mu}\left( {{i = 1},\ldots\mspace{11mu},n} \right)}},$may be replaced by the following pairs of equations:

${{\sum\limits_{{({j,i})} \in E^{\prime}}y_{ji}^{k}} = d_{ik}},{{\sum\limits_{{({i,j})} \in E^{\prime}}y_{ij}^{k}} = {d_{ik}.}}$

Note that these particular models may be completely independent for eachk, and may therefore by solved in parallel. Furthermore, these modelsmay also be generalized in the manner described in the appendix of J. A.Tomlin, “A New Paradigm for Ranking Pages on the World Wide Web”, pp350-355, Proc. World Wide Web Conference 2003 (WWW2003), Budapest, May2003.

Moreover, it may be assumed that users have independent characteristicsfor these particular models to be completely independent for each k, andconsequently the independent characteristics may not change from onetype to another. Such an assumption may be relaxed in an embodiment bymeans of dimensional reduction of the vector D_(i) of characteristics.For instance, consider that the matrix, [D_(i1), D_(i2), . . . ,D_(iM)], may be approximated by the techniques of Latent SemanticAnalysis (LSA), to give a set of vectors, [C_(i1), C_(i2), . . . ,C_(iM)], where the C_(i) may now be of the form: C_(i)=<c_(i1), c_(i2),. . . , c_(iL)>, with L<K. Such a reduction process may capture some ofthe interrelationships between the original characteristics, as well asreduce the problem computationally by using c_(i1) rather than thed_(ik) to solve L problems in parallel.

Undifferentiated trails may be used in various embodiments to modify theperformance of the method above in several ways. First, the entries ofthe matrix M′ may be determined based not on the assumption that eachoutlink of a web page may be selected uniformly, but based on actualobservations. Additionally, in the transformation from M′ to M^(e), theundifferentiated trails may be used to estimate abandonment and restartprobabilities such as described in U.S. patent application Ser. No.11/474,195 (now U.S. Pat. No. 7,624,104) entitled: “User-SensitivePageRank.”

In yet various other embodiments, unknown profile characteristics may bepropagated at step 506 from other web destinations by smoothing. FIG. 6presents an illustration depicting in an embodiment web destinationprofile characteristics propagated in a hierarchy of a set of web pagesby smoothing. There may be two types of clickstream trails illustratedin FIG. 6 that intersect a subset of web pages in a hierarchy of webpages 602. Differentiated clickstream trail 606 may representdifferentiated clickstream data and an undifferentiated clickstreamtrail 604 may represent undifferentiated clickstream data. Webdestination profile characteristics may be propagated from web pages inthe hierarchy that may intersect the differentiated clickstream trail606 to web pages in the hierarchy that may intersect theundifferentiated clickstream trail 604. Moreover, a web destination withlittle traffic may have similar content as a set of other web pages andcharacteristics may be propagated from the set of other web pages to theweb destination profile of the web page. For example, consider thefollowing static model of propagation. For any web page i, consider u(i)to be the parent of web page i in the website of i, such that u(i) mayrepresent one step up in the tree representing the website of i. Observethat u(i) may not actually exist as a web page in the graph; in thiscase, a new unvisited page may be created for u(i). Similarly, consideru^(k) to represent the composition of k applications of u, so that u²(i)may be the grandparent of i. Finally, consider u*(i) to be the set ofancestors of i. Then, A(i) may be defined to be the aggregateddistribution of the children of node i as follows:

${A(i)} = {\sum\limits_{j{{j \in {u^{*}{(i)}}}}}{p_{j}{{\overset{\sim}{D}}_{j}.}}}$For jεu*(i), consider g(i,j) to represent the generalization cost of jwith respect to i in order to represent the notion that web page j maybe significantly higher in the website than web page i and that thesubtree rooted at j may capture a much more varied distribution ofusers. The generalization cost g may capture this variance and may berepresented as the Kullback-Liebler divergence KL({tilde over (D)}_(j);{tilde over (D)}_(i)), or by some custom mechanism targeted for theparticular notion of proximity of user characteristics required by aspecific application. Recalling that w_(i) may be a measure of theconfidence of the observed distribution D_(i) at web page I, smoothingmay be performed in an embodiment as follows:

${\overset{\sim}{D}}_{i}^{1} = {{w_{i}{\overset{\sim}{D}}_{i}} + {\left( {1 - w_{i}} \right){\frac{\sum\limits_{j \in {u^{*}{(i)}}}{{g\left( {j,i} \right)}{\overset{\sim}{D}}_{j}}}{\sum\limits_{j \in {u^{*}{(i)}}}{g\left( {j,i} \right)}}.}}}$

This may be one possible model of hierarchical smoothing that may beemployed in an embodiment. Those skilled in the art will appreciate thatother types of smoothing may also be employed, such as smoothing basedon web destinations which are often visited by the same, and evenpossibly undifferentiated, user in a specific session, smoothing basedon topical similarity of the content of two web pages, smoothing basedon the similarity of click through behavior of users on two web pages,and so forth. For yet other examples, consider the work of West, M. andHarrison, J. (1998), Bayesian Forecasting and Dynamic Models,Springer-Verlag New York; pages 581-597.

In yet various additional embodiments, unknown profile characteristicsmay be propagated at step 508 from other web destination profiles ofsimilar web destinations. Even when traffic may not be observed orinferred, an approximate profile of characteristics may be generated byan analysis of the content of a web destination. Consider, for example,two websites, one of which may be observed with high traffic, and forwhich an accurate profile of characteristics may be determined. On theother hand, the traffic for the second website may be very low, but thesecond website may be quite similar to the first in terms of one or moreof content, presentation, topology, inlinks, anchortext, and so forth.In such as case, those skilled in the art may appreciate that thevisitor profiles of the two sites may be also inferred to be similar, inthe absence of other information. There may be many known techniques tocompute such similarities, such as the Jaccard coefficient, the cosinemeasure and the KL divergence. Any of these may reasonably be applied toany of the features listed above, including content, presentation,topology and so forth, to determine a similarity score between ahigh-traffic site and a low-traffic site. The profile of the mostsimilar high-traffic site may then the propagated in various embodimentsto the low-traffic site, even without any observation of the traffic atthe low-traffic site. This approach may be viewed as applying a1-nearest-neighbor classifier to classify the low-traffic website, usinghigher-traffic sites for which profiles may available as the potentialneighbors for the classifier. In a more sophisticated embodiment, it ispossible to apply other classification algorithms such as Support VectorMachines, Decision Trees, and so forth, in the same domain, based on thefeatures of the web destination such as content, presentation, topologyand so forth. This general family of techniques may represent a type ofsmoothing which may require little or no traffic data for thedestination website. Moreover, those skilled in the art may appreciatethat any measure of similarity between web destinations may be appliedin this manner without limitation. For example, a less well-studiedapproach may be applied to determine the similarity of two websitesbased on the ranks in which those sites appear in the search resultspage of search queries.

After determining characteristics of a web destination profile, the webdestination profile may be used by many applications such as onlineapplications for population-targeted advertising. For instance, anapplication that may display advertisements to users who visit a webdestination may use the present invention to select advertisements usingweb destination profiles for display with content of the webdestinations. Advantageously, an advertisement may be selected using theweb destination profile when the system may have limited knowledge ofthe content of the web destination and/or the profile of the particularuser visiting the web destination. Moreover, applications may select anadvertisement using the web destination profile and a user profile, ifavailable. Additionally, applications may select an advertisement usingthe web destination profile and using content matching for the webdestination.

FIG. 7 presents a block diagram generally representing an exemplaryarchitecture of system components in an embodiment for onlinepopulation-targeted advertising. Those skilled in the art willappreciate that the functionality implemented within the blocksillustrated in the diagram may be implemented as separate components orthe functionality of several or all of the blocks may be implementedwithin a single component. For example, the functionality for theprofile locator 716 may be included in the same component as the mappingengine 718.

In various embodiments, a client computer 702 may be operably coupled toone or more servers 710 by a network 708. The client computer 702 may bea computer such as computer system 100 of FIG. 1. The network 708 may beany type of network such as a local area network (LAN), a wide areanetwork (WAN) such as the Internet, or other type of network. Anapplication such as a web browser 704 may execute on the client computer702 and may include functionality for requesting content items at webdestinations. The web browser 704 may also include a client requesthandler 706 for sending requests for content items at web destinationsto web page servers and for receiving content items from web pageservers. The present invention operable on server 710 may supportproviding advertisements 722 selected using a web destination profile724 to the web browser 704 for display to a user.

The server 710 may be any type of computer system or computing devicesuch as computer system 100 of FIG. 1. In various embodiments, theserver may be a web page server that may provide services for providingweb pages with various content as well as advertisements selected usingweb destination profiles for the requested web pages. The server 710 mayalso include a server request handler 712 for receiving and respondingto requests for web pages with content items and an advertisementselection engine 714 for providing advertisements 722 selected using aweb destination profile 724. The advertisement selection engine 714 may,in turn, include a profile locator 716 for locating a web destinationprofile 724 for a web destination and a mapping engine 718 for mapping aweb destination profile 724 to an advertisement 722. Each of thesemodules may also be any type of executable software code such as akernel component, an application program, a linked library, an objectwith methods, or other type of executable software code.

The server 710 may include a storage 720 operably coupled to theadvertisement selection engine 714. The storage 720 may be any type ofcomputer-readable media and may store one or more advertisements 722 andweb destination profiles 724. When a URL for a web page may be displayedby a web browser 704 and may be subsequently selected by a user, arequest for a web page providing the content may be made to the server710. The server 710 may be a web page server and may also provideservices for providing advertisements selected using web destinationprofiles for the requested web pages for display to a user in additionto the content of the web pages. In various other embodiments, anotherserver configured as a web page server may provide services forproviding web pages with various content and the server 710 may provideservices for providing advertisements selected using web destinationprofiles for the requested web pages for display to the user. In thiscase, the web page server may receive a request for a web page withcontent and forward the request for the web page to the server 710, andserver 710 may respond by providing one or more advertisements 722 tothe web browser 704 for display to the user.

FIG. 8 presents a flowchart generally representing the steps undertakenin one embodiment for online population-targeted advertising. At step802, a request may be received to serve content of a web destination.For example, a request may be received by a server for providing contentof a web destination that may be a managed website of a content matchadvertising service. Such a service may manage an inventory ofadvertisements from which one or more advertisements may be presentedfor display to a visitor of the web destination.

At step 804, an advertisement may be selected using a web destinationprofile for the web destination. In an embodiment, a server may locate aweb destination profile for the web destination and then map the webdestination profile to one or more advertisements. The web destinationprofiles may be previously mapped offline to one or more advertisementsusing various techniques, including mapping a web destination profile toan advertisement with high click through rates for visitors havingcharacteristics similar to the web destination profile. In variousembodiments, a score may be associated with each advertisement that mayindicate how well the characteristics of the web destination profilematched the profile characteristics associated with each advertisement.Such a score may be used for merging with a list of advertisementsscored by using a user profile, if available, or scored by matchingcontent of the web destination.

At step 806, the advertisement may be sent in response to the requestfor content of the web destination for display with the content of theweb destination. In an embodiment, several advertisements may be sent toa web browser for display to a user. After the advertisement may be sentfor display to a user, processing may be finished for onlinepopulation-targeted advertising.

Thus the present invention may be used by applications that may displayadvertisements to users who visit a web destination, including managedcontent properties, to select advertisements using web destinationprofiles for display with content of the web destinations.Advantageously, an advertisement may be selected using the webdestination profile where the system may have limited knowledge of thecontent of the web destination and/or the profile of the particular uservisiting the web destination. Moreover, a web destination profile may beused as a prior distribution of characteristics to estimatecharacteristics of a user that the system may know little about, forinstance, based on some user-specific behavior such as recent browsinghistory of the user. In addition to employing the techniques describedfor web destination profiling as part of an application or system forselecting advertisements, those skilled in the art will appreciate thatany number of the specific techniques described for web destinationprofiling may be used as a standalone technique to selectadvertisements.

As can be seen from the foregoing detailed description, the presentinvention provides an improved system and method for web destinationprofiling for online population-targeted advertising. The system andmethod may analyze traffic at a particular web destination in order tounderstand the population visiting the web destination and may determineunknown characteristics of the population visiting the web destinationusing a variety of techniques including inferring characteristics bymodeling traffic flow through other web destinations, estimatingcharacteristics from other web destination profiles by predictingtraffic flow through other web destinations, propagating characteristicsto a web destination profile by smoothing a joint distribution ofcharacteristics of other web destination profiles, and so forth.Advantageously, any number of these techniques may be used to selectadvertisements. Even when traffic may not be observed or inferred,unknown profile characteristics may be propagated from other webdestination profiles of similar web destinations. As a result, thesystem and method provide significant advantages and benefits needed incontemporary computing and in online applications.

While the invention is susceptible to various modifications andalternative constructions, certain illustrated embodiments thereof areshown in the drawings and have been described above in detail. It shouldbe understood, however, that there is no intention to limit theinvention to the specific forms disclosed, but on the contrary, theintention is to cover all modifications, alternative constructions, andequivalents falling within the spirit and scope of the invention.

What is claimed is:
 1. A computer-implemented method for profiling a webdestination, comprising: using a processor device, performing: analyzingtraffic at a web destination for determining unknown characteristics ofa population of visitors to said web destination, said trafficcomprising both differentiated and undifferentiated clickstream trails,wherein said undifferentiated clickstream trails comprise clickstreamdata that is undifferentiated as to the visitors to the web destination;generating a distribution of characteristics of the population ofvisitors to the web destination; generating a web destination profileover the distribution of characteristics; propagating the webdestination profile with the distribution of characteristics fromprofiles of other web destinations by following the clickstream trails;and storing the web destination profile in storage.
 2. The method ofclaim 1 further comprising determining known characteristics of thepopulation of visitors to the web destination for the web destinationprofile.
 3. The method of claim 1 wherein determining the unknowncharacteristics of the population of visitors to the web destination forthe web destination profile comprises inferring at least onecharacteristic to the web destination profile from other web destinationprofiles by modeling traffic flow through other web destinations.
 4. Themethod of claim 1 wherein determining the unknown characteristics of thepopulation of visitors to the web destination for the web destinationprofile comprises estimating at least one characteristic of the webdestination profile from other web destination profiles by predictingtraffic flow through other web destinations.
 5. The method of claim 4wherein estimating the at least one characteristic of the webdestination profile from other web destination profiles by predictingtraffic flow through other web destinations comprises determining a sumof a distance between a predicted distribution and an observeddistribution and a distance between the predicted distribution and apropagated distribution determined by the traffic arriving at the webdestination.
 6. The method of claim 4 wherein estimating the at leastone characteristic of the web destination profile from other webdestination profiles by predicting traffic flow through other webdestinations comprises determining a maximal entropy for an entropymodel of the other web destinations.
 7. The method of claim 1 whereindetermining the unknown characteristics of the population of visitors tothe web destination for the web destination profile comprisespropagating at least one characteristic to the web destination profileby smoothing a joint distribution of characteristics of other webdestination profiles.
 8. The method of claim 7 wherein propagating theat least one characteristic to the web destination profile by smoothingthe joint distribution of characteristics of other web destinationprofiles comprises smoothing a joint distribution of characteristics ofother web destination profiles for web destinations visited by a user ina session.
 9. The method of claim 7 wherein propagating the at least onecharacteristic to the web destination profile by smoothing the jointdistribution of characteristics of other web destination profilescomprises smoothing a joint distribution of characteristics of other webdestination profiles for web destinations selected by topical similarityof content.
 10. The method of claim 7 wherein propagating the at leastone characteristic to the web destination profile by smoothing the jointdistribution of characteristics of other web destination profilescomprises smoothing a joint distribution of characteristics of other webdestination profiles for web destinations selected by similarity ofclickthrough behavior of visitors.
 11. A non-transitorycomputer-readable medium having computer-executable instructions forperforming the method of claim
 1. 12. A computer system for profiling aweb destination, comprising: storage storing a web destination profile;memory with computer-executable instructions stored thereon; and aprocessor device operably coupled with the memory, executing thecomputer-executable instructions comprising: analyzing traffic at theweb destination for determining at least one unknown characteristic of apopulation of visitors to said web destination, the traffic comprisingboth differentiated and undifferentiated clickstream trails, whereinsaid undifferentiated clickstream trails comprises clickstream data thatis undifferentiated as to the visitors to the web destination;determining a distribution of characteristics of the population ofvisitors to the web destination; generating the web destination profileover the distribution of characteristics for the population of visitors;and propagating the web destination profile with the distribution of thecharacteristics from profiles of other web destinations by following theclickstream trails.
 13. The system of claim 12 wherein thecomputer-executable instructions further comprise: analyzing theclickstream data from the web destination.
 14. The system of claim 12further comprising a topology analysis engine for analyzing topologydata about the web destination.
 15. The system of claim 12 furthercomprising a model generator for generating a model of traffic flowamong a plurality of web destinations.
 16. The system of claim 15further comprising a traffic flow analysis engine for propagatingpopulation characteristics to a plurality of web destination profiles bypredicting traffic flow through the plurality of web destinations.